Hilal Daglar, Ph.D. is a computational research scientist at the Materials Discovery Research Institute. In her current role, she focuses on computational modeling and the discovery of porous materials for gas adsorption and separation applications. Her work integrates molecular simulations, machine learning, and high-throughput computational workflows to evaluate structural stability, adsorption performance, and structure–property relationships in experimentally relevant materials.

Daglar’s postdoctoral research at the University of Chicago centered on molecular modeling of porous frameworks, with a focus on water diffusion and adsorption in flexible materials, CO₂ capture in covalent organic frameworks, and hydrogen storage in porous solids. Her work emphasized physically realistic structural models and the validation of computational predictions against experimental characterization data.

During her doctoral and predoctoral training, Daglar conducted advanced computational research at Northwestern University. In this role, she worked on molecular modeling and machine learning assisted studies of water adsorption, framework flexibility effects, and hydrogen storage in porous materials, contributing to large-scale computational screening and data-driven materials evaluation.

Daglar received her B.S. in chemical engineering from Istanbul Technical University and earned her Ph.D. in chemical and biological engineering from Koç University. During her graduate studies, she focused on high-throughput computational screening of metal–organic frameworks and related materials for gas separation and storage applications. Her research combined molecular simulations and machine learning to uncover structure–performance relationships across large materials datasets. She also gained early research experience in membrane science and process design during her undergraduate training in chemical engineering.